乾 友彦

BACKGROUND: Some attempts have been made to detect atrial fibrillation (AF) with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions. OBJECTIVE: This study is the second part of a 2-phase study aimed at developing a method for immediate detection of paroxysmal AF, using a wearable device with built-in photoplethysmography (PPG). The objective of this study is to develop an algorithm to immediately diagnose AF by an Apple Watch equipped with a PPG sensor that is worn by patients undergoing cardiac surgery and to use machine learning on the pulse data output from the device. METHODS: A total of 80 patients who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative AF, using a telemetry-monitored electrocardiogram (ECG) and an Apple Watch. AF was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on the pulse rate data output from the Apple Watch. RESULTS: One of the 80 patients was excluded from the analysis due to redness caused by wearing the Apple Watch. Of 79 patients, 27 (34.2%) developed AF, and 199 events of AF including brief AF were observed. Of them, 18 events of AF lasting longer than 1 hour were observed, and cross-correlation analysis showed that pulse rate measured by Apple Watch was strongly correlated (cross-correlation functions [CCF]: 0.6-0.8) with 8 events and very strongly correlated (CCF>0.8) with 3 events. The diagnostic accuracy by machine learning was 0.9416 (sensitivity 0.909 and specificity 0.838 at the point closest to the top left) for the area under the receiver operating characteristic curve. CONCLUSIONS: We were able to safely monitor pulse rate in patients who wore an Apple Watch after cardiac surgery. Although the pulse rate measured by the PPG sensor does not follow the heart rate recorded by telemetry-monitored ECGs in some parts, which may reduce the accuracy of AF diagnosis by machine learning, we have shown the possibility of clinical application of using only the pulse rate collected by the PPG sensor for the early detection of AF.

症例は48歳男性。解離性大動脈瘤(DeBakey III B型)に対し25歳時に下行大動脈置換術、27歳時に上行大動脈瘤に対し上行大動脈+部分弓部大動脈置換術、28歳時に残存解離による胸腹部大動脈瘤に対し胸腹部大動脈置換術を施行され外来で経過観察されていた。3回目の手術時に腹腔動脈・上腸間膜動脈・右腎動脈がVisceral aortic patch(VAP)を用いて再建されており、同部の残存している自家大動脈壁に瘤化(最大径70mm×61mm)を認めたため手術の方針となった。2回の左開胸手術後であったため2期的にhybrid aortic repairを施行する方針とし1回目にreno-visceral debranching手術、2回目にステントグラフト内挿術を行い、瘤の空置に成功した。(著者抄録)